Introduction to Deep Learning in Python
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
Start Course for Free4 Hours17 Videos50 Exercises200,375 Learners
3500 XPCreate Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).Loved by learners at thousands of companies
Course Description
Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.
- 1
Basics of deep learning and neural networks
FreeIn this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today. You'll build simple neural networks and generate predictions with them. - 2
Optimizing a neural network with backward propagation
Learn how to optimize the predictions generated by your neural networks. You'll use a method called backward propagation, which is one of the most important techniques in deep learning. Understanding how it works will give you a strong foundation to build on in the second half of the course. - 3
Building deep learning models with keras
In this chapter, you'll use the Keras library to build deep learning models for both regression and classification. You'll learn about the Specify-Compile-Fit workflow that you can use to make predictions, and by the end of the chapter, you'll have all the tools necessary to build deep neural networks. - 4
Fine-tuning keras models
Learn how to optimize your deep learning models in Keras. Start by learning how to validate your models, then understand the concept of model capacity, and finally, experiment with wider and deeper networks.
Hugo Bowne-Anderson
Yashas RoyPrerequisites
Supervised Learning with scikit-learn
Dan Becker
Data Scientist and contributor to Keras and TensorFlow libraries
Dan Becker is a data scientist with years of deep learning experience. He has contributed to the Keras and TensorFlow libraries, finishing 2nd (out of 1353 teams) in the $3million Heritage Health Prize competition, and supervised consulting projects for 6 companies in the Fortunate 100. He previously worked as a data scientist at Google. Now he is the CEO and co-founder of Decision.ai, which helps companies apply their machine learning models to make better real-world decisions.
What do other learners have to say?
“I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyds Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics, USAA
Join over 9 million learners and start Introduction to Deep Learning in Python today!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).